Responsive action prediction based on electronic messages among a system of networked computing devices

ABSTRACT

Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to facilitate implementation of an interface, and, more specifically, to a computing and data storage platform that implements specialized logic to predict an action based on content in electronic messages, at least one action being a responsive electronic message. In some examples, a method may include receiving data representing an electronic message with an electronic messaging account, identifying one or more component characteristics associated with one or more components of the electronic message, characterizing the electronic message based on the one or more component characteristics to classify the electronic message for a response as a classified message, causing a computing device to perform an action to facilitate the response to the classified message, and the like.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of copending U.S. patentapplication Ser. No. 16/827,625, filed Mar. 23, 2020 and entitled,“RESPONSIVE ACTION PREDICTION BASED ON ELECTRONIC MESSAGES AMONG ASYSTEM OF NETWORKED COMPUTING DEVICES”, U.S. patent application Ser. No.16/827,625 is a continuation application of U.S. patent application Ser.No. 15/821,543, filed Nov. 22, 2017, now U.S. Pat. No. 10,601,937 andentitled, “RESPONSIVE ACTION PREDICTION BASED ON ELECTRONIC MESAGESAMONG A SYSTEM OF NETWORKED COMPUTING DEVICES”, all of which is hereinincorporated by reference in its entirety for all purposes.

FIELD

Various embodiments relate generally to data science and data analysis,computer software and systems, and control systems to provide a platformto facilitate implementation of an interface, and, more specifically, toa computing and data storage platform that implements specialized logicto predict an action based on content in electronic messages, at leastone action being a responsive electronic message.

BACKGROUND

Advances in computing hardware and software have fueled exponentialgrowth in delivery of vast amounts of information due to increasedimprovements in computational and networking technologies. Also,advances in conventional data storage technologies provide an ability tostore increasing amounts of generated data. Thus, improvements incomputing hardware, software, network services, and storage havebolstered growth of Internet-based messaging applications, such associal networking platforms and applications, especially in an area ofgenerating and sending information concerning products and services.Unfortunately, such technological improvements have contributed to adeluge of information that is so voluminous that any particular messagemay be drowned out in the sea of information. Consequently, providers ofgoods and services are typically inundated with messages concerningcustomer service-related matters via social networking platforms. Brandreputations and brand loyalty may be jeopardized if providers of goodsand services are impeded from filtering through a multitude of messagesto identify a relatively small number of critical messages.

In accordance with some conventional techniques, creators of content andinformation, such as manufacturers and merchants of products orservices, have employed various techniques to review numerous messagesto identify content that might be of critical nature. However, whilefunctional, these techniques suffer a number of other drawbacks.

The above-described advancements in computing hardware and software havegiven rise to a relatively large number of communication channelsthrough which information may be transmitted to the masses. For example,information may be transmitted via a great number of messages throughtext messages, website posts, social networking messages, and the like.However, social networking platforms are not well-suited to leveragesocial media to address customer service-related issues as social mediaplatforms were initially formed to principally connect persons sociallyrather than commercially. For example, various conventional approachesto reviewing numerous social-related messages typically are resourceintensive, requiring human reviewers to read a message and determinesome type of dispositive action, which typically may be less repeatableand subject to various levels of skill and subjectivity applied toidentifying messages that may be important to discover. Further, it isnot unusual for the traditional approaches to consume relatively largequantities of computational resources and time, among other things.

Thus, what is needed is a solution for facilitating techniques topredict an action based on electronic messages, without the limitationsof conventional techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments or examples (“examples”) of the invention aredisclosed in the following detailed description and the accompanyingdrawings:

FIG. 1 is a diagram depicting an electronic message response platform,according to some embodiments;

FIG. 2 depicts another example of an electronic message responseplatform, according to various examples;

FIG. 3 is a flow diagram as an example of predicting at least one actionfor generating a response electronic message, according to someembodiments;

FIG. 4 is a diagram depicting an example of an electronic messageresponse platform configured to collect and analyze electronic messagesto model predictive responses, according to some examples;

FIG. 5 is a diagram depicting an example of a data correlator and amessage characterizer configured to determine a predictive response,according to some embodiments;

FIG. 6 is a flow diagram as an example of forming a set of patterneddata to predict whether an electronic message generates a response,according to some embodiments;

FIG. 7 is a diagram depicting an example of a user interface configuredto accept data signals to visually identify and/or interact with asubset of messages predicted to generate a response, according to someexamples;

FIG. 8 is a diagram depicting an example of a user interface configuredto aggregation of messages predicted to at least generate a response,according to some examples; and

FIG. 9 is a diagram depicting an example of an electronic messageresponse platform configured to harvest and analyze electronic messages,according to some examples.

DETAILED DESCRIPTION

Various embodiments or examples may be implemented in numerous ways,including as a system, a process, an apparatus, a user interface, or aseries of program instructions on a computer readable medium such as acomputer readable storage medium or a computer network where the programinstructions are sent over optical, electronic, or wirelesscommunication links. In general, operations of disclosed processes maybe performed in an arbitrary order, unless otherwise provided in theclaims.

A detailed description of one or more examples is provided below alongwith accompanying figures. The detailed description is provided inconnection with such examples, but is not limited to any particularexample. The scope is limited only by the claims, and numerousalternatives, modifications, and equivalents thereof. Numerous specificdetails are set forth in the following description in order to provide athorough understanding. These details are provided for the purpose ofexample and the described techniques may be practiced according to theclaims without some or all of these specific details. For clarity,technical material that is known in the technical fields related to theexamples has not been described in detail to avoid unnecessarilyobscuring the description.

FIG. 1 is a diagram depicting an electronic message response platform,according to some embodiments. Diagram 100 depicts an example of anentity computing system 150 configured to, among other things, predictwhether an electronic message 174 received by an electronic messageresponse platform 160 performs, or is likely to cause performance of, anaction responsive to the contents of electronic message 174, accordingto various embodiments. In at least one implementation, electronicmessage response platform 160 may be configured to determinepredictively whether to generate a response (e.g., a response electronicmessage) using a model formed, for example, based on historic behaviorand/or activity. Examples of historic behavior and/or activity includepast user inputs to, for example, generate a response relative to apreviously received electronic message. Electronic message responseplatform 160 may be configured to predict responses for electronicmessages 119 a (e.g., intra-system messages), as well as for electronicmessages 119 b (e.g., inter-system messages) or any other message. Thus,electronic messages 119 a and 119 b need not be directed to electronicmessage response platform 160, which may capture the content from thesemessages to predict a response.

Further, electronic message response platform 160 may be configured todetermine a value indicative of a likelihood of specific messagecontents to generate a response or perform other actions. For example, areceived message may be further processed by applying or linking tags(e.g., metadata) to electronic message 174 and/or to any constituentcomponents to identify such components. A component of a message mayinclude, according to various examples, symbols (e.g., a letter ornumber), a word, a group of words, such as a phrase, a message topic, orany other characteristic of the electronic message associated with, ordescriptive of, the message (as well as the message itself). Hence,electronic message response platform 160 may be configured toautomatically route a received message to a subset of computing devicesfor refined resolution. As an example, a subset of computing devices maybe associated with one or more agents who perform common tasks, such asreviewing a message in a specific language (e.g., electronic messagesusing Mandarin).

Diagram 100 depicts an entity computing system 150 including a userinterface 120 and a computing device 130 (e.g., one or more servers,including one or more processors and/or memory devices), both of whichmay be configured to generate “response” messages that may be configuredfor users 108 a, 108 b, 108 c, and 108 d. Any one or more of messagenetwork computing systems 110 a and 110 b may be configured to receiveand transmit electronic messages, regardless of a context, to convey anexperience, observation, request for assistance (e.g., in relation to aproduct or service), or any other information with or among any numberof users for any reason. One or more of message network computingsystems 110 a and 110 b may be configured to distribute electronicmessage content in any form in any digital media or channel 107. Invarious examples, message network computing systems 110 a and 110 b mayinclude any number of computing systems configured to propagateelectronic messaging, including, but not limited to, computing systemsincluding third party servers, such as third parties like Facebook™,Twitter™, LinkedIn™, Instagram™, Snapchat™, as well as other private orpublic social networks to provide social-media related informationaldata exchange services. Computing systems 113 a and 113 b may beconfigured to provide any type of digital content, such as email, textmessaging (e.g., via SMS messages), web pages, audio, video (e.g.,YouTube™), etc.

According to some examples, message network computing systems 110 a and110 b may include applications or executable instructions configured toprincipally facilitate interactions (e.g., social interactions) amongstone or more persons, one or more subpopulations (e.g., private groups orpublic groups), or the public at-large. Examples of message networkcomputing systems 110 a and 110 b, as channels 107, may include theabove-mentioned electronic accounts for Facebook™, Twitter™, LinkedIn™,Instagram™, and Snapchat™, as well as YouTube™, Pinterest™, Tumblr™,WhatsApp™ messaging, or any other platform configured to promote sharingof content, such as videos, audio, or images, as well as sharing ideas,thoughts, etc. in a socially-based environment. According to someexamples, content source computing systems 113 a and 113 b may includeapplications or executable instructions configured to principallypromote an activity, such as a sports television network, a professionsports team (e.g., a National Basketball Association, or NBA®, team), anews or media organization, a product producing or selling organization,and the like. Content source computing systems 113 a and 113 b mayimplement websites, email, chatbots, or any other digital communicationchannels, and may further implement electronic accounts to conveyinformation via message network computing systems 110 a and 110 b.

In view of the structures and/or functionalities of message networkcomputing systems 110 a and 110 b and content source computing systems113 a and 113 b, an electronic message may include a “tweet” (e.g., amessage via a Twitter™ computing system), a “post” (e.g., a message viaa Facebook™ computing system), or any other type of social network-basedmessages, along with any related functionalities, such as forwarding amessage (e.g., “retweeting” via Twitter™), sharing a message,associating an endorsement of another message (e.g., “liking” a message,such as a Tweet™, or sharing a Facebook™ post, etc.), and any otherinteraction that may convey a “response” to one or more electronicaccounts at increased rates of transmissions or propagation to addressconcerns or statements that may otherwise affect a reputation of abrand. According to various examples, an electronic message received viaa network 111 can include any type of digital messaging that can betransmitted over any digital network.

According to some embodiments, entity computing system 150 or electronicmessage response platform 160, or both, may be configured to facilitategeneration of an electronic message (e.g., a response electronic) basedon predicting a likelihood of generating a response, or, alternatively,a dismissal of received message 174. In some examples, electronicmessage response platform 160 may be configured to characterize anelectronic message and/or its contents (e.g., components) to identify aclassification value that may be associated with the electronic message.For example, an electronic message 174 to entity computing system 150may include content directed to a “racing bike retailer” (e.g., anelectronic account identifier, such as @Kaneolli_Bikes), and the contentmay specify that a “wrong order number” has been used for an on-linepurchase in association with a specific “customer account identifier.”Electronic message response platform 160 may be configured to analyzethe electronic message to characterize components “racing bikeretailer,” “wrong order number,” and “customer account identifier.” Thecharacterized component values can be used to identify a “classificationvalue” relative to, for example, a dataset includes data having similarcharacterized component values. According to some examples, a“classification” value may be a value specifying whether a response tothe electronic message may be generated or dismissed. In a specificexample, a classification value may be representative of a likelihood orprobability of generating a response.

Entity computing system 150 is shown to include a computing device 120and display configured to generate a user interface, such as a messageresponse interface 122. Entity computing system 150 also includes aserver computing device 130, which may include hardware and software, ora combination thereof, configured to implement an electronic messageresponse platform 160 (or “response platform 160”), according to variousexamples. Response platform 160 may include a message characterizer 137configured to characterize one or more received electronic messages(e.g., messages 119 a, 119 b, and 174 received via a network 111), aswell as components, to identify a value indicative of a predicativeresponse. Message characterizer 137 is shown to include a classifier 138and a response predictor 139. Classifier 138 may be configured toclassify a received electronic message as generating an action.Classifier 138 may be configured to classify a message as generating aresponse based on a threshold value (or range of values). In someexamples, a threshold value may be formed empirically or theoreticallybased on historic actions of responses or dismissals as actionsperformed in relation to previously-received messages. Responsepredictor 139 may be configured to invoke an action, such as sending aresponse, dismissing the received electronic message, modifying athreshold value (e.g., setting a “risk tolerance threshold”), routingthe received electronic message to a subset of computing devices (e.g.,computing devices optimized to address messages in a certain language orfor specific products) for refined processing or resolution, and thelike. Also, electronic message response platform 160 may therefore beconfigured to generate and send response electronic messages to any ofplatforms 110 a, 110 b, 113 a, and 113 b.

To illustrate a functionality of response platform 160, consider anexample in which a user 121, as an agent, may receive an electronicmessage 124 for presentation via message response interface 122, wherebyelectronic message 124 may originate at one or more computing systems110 a, 110 b, 113 a, and 113 b. According to various examples, responseplatform 160 may be configured to determine a classification value forelectronic message 124 to determine whether a response may be predicted.In this example, response platform 160 may determine electronic message124 has a classification value of 0.81, as shown as visual identifier(“81%”) 125, which exceeds an example threshold value of 0.80 (notshown). In some cases, if electronic message 124 is associated with alesser value, such as 0.70, then response platform 160 may be configuredto deemphasize, deprioritized, and/or dismiss presentation of electronicmessage 124 as well as any other action related thereto. Thus,computational resources and other resources may be preserved forhandling numerous incoming electronic messages by predicting an amountof messages (e.g., a configurable amount) that may generate a response(e.g., relative to electronic messages that may be dismissed).

According to some embodiments, user 121 may interact with computingdevice 120 to generate a response electronic message 172. Responseplatform 160 may automatically generate a field into which user 121 mayenter input to form a response electronic message (not shown). Also,response platform 160 may include logic configured to analyze andevaluate electronic message 124, prior to generating a message, todetermine, for example, whether electronic message 124 may beautomatically routed or forwarded to other one or more computing devices120 associated users 121. For example, certain other computing devices120 (not shown) may be designated or optimized to facilitate responsemessage generation based on an attribute of electronic message 124. Anattribute may indicate a particular language, such as German, aparticular product, such as a “racing bike model 2XQ,” demographicprofile data about a sender of a message (e.g., originator's geographiclocation), and the like. Hence, resolution of a response may includeautomatically routing electronic message 124 to a computing device 120based on an attribute.

In some implementations, user 121 may initiate or facilitatefunctionalities regarding the processing of a response message.Optionally, a user 121 may cause a selection device 126 to hover over orselect graphical representation 125. In response, one or more messageperformance actions 123 may be presented to user 121. Here, responseactions graphic representation 123 include a response confirmation inuser interface portion 127 and a refined resolution selection portion128. User 121 may cause selection device 126 to select user input(“yes”) 129 a to initiate generation of a response message. In someexamples, user 121 may cause selection of user input (“no”) 129 b to“dismiss” generation of a response message. Further, user 121 may causeselection device 126 to select user input (“yes”) 129 c for initiatingrefined resolution of electronic message 174. In some examples, refinedresolution may include routing to another computing device 120 forfurther refinements, such as generating a response electronic messagebased on language, product type, geographic location, or any othercomponent characteristic or attribute.

Diagram 100 further depicts response platform 160 being coupled tomemory or any type of data storage, such as data repositories 142 and144, among others. Message data repository 142 may be configured tostore any number of electronic messages 172 and 174 received (e.g.,previously-received), generated, and transmitted by response platform160. For example, response platform 160 may be configured to storemultiple received electronic messages 174 (e.g., as historic archivaldata). Also, response platform 160 may be configured to determinecharacteristics or attributes of one or more components of receivedelectronic messages. According to some examples, a component of anelectronic message may include a word, a phrase, a topic, or any messageattribute, which can describe the component. For example, a messageattribute may include metadata that describes, for example, a languageassociated with the word, or any other descriptor, such as a synonym, alanguage, a user characteristic (e.g., age, gender identity, etc.), areading level, a geographic location, and the like. Message attributesmay also include values of one or more classification values (e.g., oneor more values may predict an action, such as generating a response,etc.). Components of messages may be tagged or otherwise associated withany of the above-described metadata.

Continuing with the example of diagram 100, classification values may bederived based on model data, including an account's historical behavioror activity relative to a set of characterized attribute data. The modeldata may be stored in repository 144, along with classification values,threshold values, and the like.

FIG. 2 depicts another example of an electronic message responseplatform, according to various examples. Diagram 200 depicts a responseplatform 260 including a data collector 230, which, in turn, includes anatural language processor 232, an analyzer 234, and a message generator262. Response platform 260 may be configured to receive data 201 a,which may include electronic message data from a particular user accountor from any number of other electronic accounts (e.g., social mediaaccounts, email accounts, etc.). Further, response platform 260 may beconfigured to publish or transmit an electronic message 201 c vianetwork 211 to any number of message networked computing devices (notshown). In one or more implementations, elements depicted in diagram 200of FIG. 2 may include structures and/or functions as similarly-named orsimilarly-numbered elements depicted in other drawings.

Data collector 230 is configured to detect and parse the variouscomponents of an electronic message, and further is configured toanalyze the characteristics or attributes of each component. Datacollector 230 is shown to include a natural language processor 232 and amessage component attribute determinator 233. Natural language processor232 may be configured to ingest data to parse portions of an electronicmessage (e.g., using word stemming, etc.) for identifying components,such as a word or a phrase. Also, natural language processor 232 may beconfigured to derive or characterize a message as being directed to aparticular topic based on, for example, sentiment analysis techniques,content-based classification techniques, and the like. In some examples,natural language processor 232 may be configured to apply word embeddingtechniques in which components of an electronic message may berepresented as a vector.

Message component attribute determinator 233 may be configured toidentify characteristics or attributes, such as message attribute data203, for a word, phrase, topic, etc. In various examples, messageattribute data 203 may be appended, linked, tagged, or otherwiseassociated with a component to enrich data in, for example, message datarepository 242 and/or model data repository 244. A classification valuemay be a characteristic or an attribute of a message component, and thusmay be used as a tag. Examples of message attribute data 203 aredepicted as classification data 203 a (e.g., an attribute specifyingwhether a component or message may be classified as generating, forexample, a response or dismissal), media type data 203 b (e.g., anattribute specifying whether a component may be classified as beingassociated with an email, a post, a webpage, a text message, etc.),channel type data 203 c (e.g., an attribute specifying whether acomponent may be associated with a type of social networking system,such as Twitter), and the like. Message attribute data 203 may alsoinclude context metadata 203 d, which may include attributes thatspecify environmental data or contextual data, such as a context inwhich an electronic message is received or a response is generated.Thus, context metadata 203 d may include data representing a time ofday, a year, a season, a service-related context, a payment-relatedcontext, etc. Also, a tag including metadata 203 d may refer to acontext in which a word is used in a transmission of a number ofelectronic messages (e.g., a tag indicating a marketing campaign, or thelike). Also, a tag including metadata 203 d may refer to an industry oractivity (e.g., a tag indicating an electronic message componentrelating to autonomous vehicle technology, or basketball), etc.Furthermore, message attribute data 203 may also include profile data203 e, which may include attributes that describe, for example,demographic data regarding an author of a received electronic message,or the like. Other metadata 203 f may be associated with, or tagged to,a word or other message component. As such, other metadata 203 f mayinclude a tag representing a language in which the word is used (e.g., atag indicating English, German, Mandarin, etc.). In some cases, othermetadata 203 d may include data representing values of computedthreshold values or classification values (e.g., a tag may indicate avalue of an amount of likelihood of generating a response, etc.).Message attribute data 203, and the corresponding tags, may be stored inmessage data repository 242.

Analyzer 234 may be configured to characterize various components todiscover characteristics or attributes related to a component, and mayfurther be configured to characterize a message as having an associatedclassification value (e.g., probability). Analyzer 234 includes amessage characterizer 237, which, in turn, may include a classifier 238and a response predictor 239. Classifier 238 may be configured toclassify a received electronic message as being configured to generatean action, such as generating a response, based on a classificationvalue relative to a threshold value (or range of values). Aclassification value, at least in some examples, may be derived bymatching a pattern of data for a received electronic message against adata model stored in repository 244. For example, a data model mayinclude patterns of vector data specifying characteristic or attributevalues related to a corresponding classification value. In someexamples, a threshold value may be formed empirically or theoreticallybased on historic actions of responses or dismissals and integrated intoa data model. Response predictor 239 may be configured to invoke anaction, such as sending a response, dismissing the received electronicmessage, modifying a threshold value, routing the received electronicmessage to a subset of computing devices for processing or resolution,and the like.

Diagram 200 further depicts response platform 260 including a messagegenerator 262 configured to generate response messages. According tosome examples, message generator 262 may include a refined responsemanager 264 that may be configured to automatically or manually causefurther processing and refinement of a response message. A computingdevice configured to perform specific tasks relating to a characteristicof a message (e.g., messages relating to finances, including billing,invoicing, refunding, etc.) may be designated as a destination to whicha subset of electronic messages may be directed. Thus, reduced instancesof specialized software applications or a reduced set of skilled usersmay efficiently generate a response message. Message generator 262 maybe configured further to generate any number of platform-specificresponse messages. Thus, message generator 262 may generate anelectronic message or content formatted as, for example, a “tweet,” aFacebook™ post, a web page update, an email, etc.

FIG. 3 is a flow diagram as an example of predicting at least one actionfor generating a response electronic message, according to someembodiments. Flow 300 may be an example of predictively determiningwhether to generate a response (e.g., a response electronic message)using a model formed, for example, based on historic behavior and/oractivity (e.g., past control signals, user inputs, etc. in relation toprior messages-handling actions). At 302, data representing anelectronic message may be received by an entity computing system (orelectronic messaging account), the electronic message including datarepresenting an item (e.g., content, topic, etc. of electronic message).Examples of entity computing systems including any subset of computingdevices and memory storage devices implemented to manage, for example, aflow of electronic messages that may affect perception (e.g. brandmanagement) or usage (e.g., customer service) of a product or serviceprovided by an organization or individual as an entity (e.g., amanufacturer, a seller, etc.). Examples of electronic messaging accountsinclude data structures implemented in association with socialnetworking platforms, such as accounts implemented for Facebook™,Twitter™, LinkedIn™, Instagram™, and Snapchat™, as well as other messageor information-exchange applications or other platforms. Note that anelectronic message may be transmitted to an electronic messaging accountassociated with either an entity computing system or a third partycomputing system. In the latter case, an entity computing system may“harvest” or “scrape” accessible social media-related messages notdirected to the entity to identify whether to respond to a third partymessage associated with a third-party electronic account.

At 304, one or more component characteristics (e.g., electronic messageattributes) associated with one or more components of an electronicmessage may be identified. Examples of component characteristicsinclude, but are not limited to, data representing one or more of alanguage, a word, and a topic specifying a product or a service, aquality issue, a payment issue, or any other attribute specifying acharacteristic of the content of an electronic message. Further, anelectronic message may be characterized to identify a componentcharacteristic value, which may be appended to data representing acomponent (e.g., appended as a tag, link, etc.). Hence, a tag specifyinga component characteristic value may be used to determine whether toperform an action as a function of the component characteristic value.In one example, an action includes routing the electronic message to anagent computing system to generate a response. To illustrate, considerthe following example in which electronic message is characterized to bewritten in the “French” language, which may be abbreviated as “FR.” Atag “FR” may be applied to one or more components of an electronicmessage (as well as the message itself). An entity computing system,upon identifying the tag “FR,” may be configured to route the message toa subset of agent computing devices at which French language-based itemsmay be processed.

At 306, an electronic message may be characterized based on one or morecomponent characteristics to classify whether an electronic message isclassified as a message that likely initiates a response. For example,data representing component characteristics may be matched against asubset of patterns of data in a data model (e.g., using pattern matchingtechniques), the subset of patterns being associated with a likelihoodthat a specific pattern of data (of a received message) may likely causea response. Thus, an electronic message may be classified as beingassociated with, for example, a first classification value thatspecifies generation of a responsive electronic message. In variousexamples, the content of a response message may include anautomatically-generated acknowledgment of message receipt, an artificialintelligence (“AI”)-generated response, or a human agent-generatedresponse.

A first classification value may be determined (e.g., computed) forclassifying an electronic message, whereby the first classificationvalue represents a likelihood (e.g., a probability) of an action. Anexample of an action includes a generated electronic message responsiveto at least one of the components, such as topics including “a price,”“a product refund,” “store hours of operation,” “a malfunctioningproduct,” etc. To classify an electronic message, a first thresholdvalue may be retrieved, for example, from data storage. For example, athreshold value may include a value of “0.80,” which may specify that anelectronic message is classified as probabilistically generating aresponse if the first classification value meets or exceeds “0.80.” Soto determine whether a first classification applies to a receivedelectronic message, a value, such as “0.89” associated with anelectronic message, may be compared against a first threshold value of“0.80.” Thus, if a value meets the threshold value, the electronicmessage may be classified such that the value is identified as a firstclassification value. Note that an electronic message may be associatedwith more than one classification, according to some examples.

At 306, a second threshold value may be identified against which tocompare with a value to classify the electronic message, according tosome examples. In one instance, a second threshold value may bedescribed as a “risk tolerance threshold” value. In some cases, a secondthreshold value may be derived by varying a first threshold value to,for example, adjust the strictness or tolerance with whichdeterminations to generate response messages are performed (e.g.,varying a threshold value of “0.80” to “0.75”). By increasing atolerance, a subset of classifications for another value associated witha response may be identified. For example, a relaxed second thresholdvalue may facilitate detection of “false negatives” (e.g., messages thatinitially do not meeting a first threshold value). Accordingly, anelectronic message (or portion thereof) may be classified as having asecond classification value based on a second threshold value. Inresponse, an evaluation may be performed to identify whether anelectronic message has been classified initially as a false negative(e.g., a determination that incorrectly identifies an electronic messageas predictively “not” generating a response relative to a firstclassification value, when at least in some cases the electronic messageought to generate a response). By determining that an electronic messageshould generate a response, the electronic message may be reclassifiedto form a reclassified electronic message. The reclassification of themessage (e.g., based on an input to generate a response) may cause adata model to recalibrate so at to characterize other electronicmessages (e.g., similar messages in the future) to increase accuracy ofpredicting the first classification value.

At 306, a third threshold value may be identified against which tocompare with a value to classify the electronic message, according tosome examples. The value can be compared against a third threshold valueto classify an electronic message as, for example, “dismissed.” Upondetecting the third classification value, the electronic message maypredictably yield a dismissal. In some cases, dismissal of a message maybe an action (or inaction). To illustrate, consider an electronicmessage is computed to be associated with first value of “0.35,” wherebya “dismiss” threshold value may be “0.45.” Hence, the electronic messagemay be dismissed in which, for example, no response is generated (or aresponse is suppressed or otherwise rejected). According to variousexamples, the third threshold value may be modified to vary thethreshold value as a function of any parameter.

At 308, a computing device including a user interface may be invoked toperform an action, such as generating or refining a response, tofacilitate the response to a classified message. Note that in someexamples, a user interface need not be invoked to perform an action. Anaction need not be performed, for example, if an item is identified ashaving an attribute for which value is indeterminate, negligible, orotherwise is not valuable. In this case, an item need not be presentedto a user. At 310, a user input may be presented on the user interfaceconfigured to accept a data signal to initiate the action. For example,a user input may specify whether to “respond” or “dismiss” a receivedelectronic message.

FIG. 4 is a diagram depicting an example of an electronic messageresponse platform configured to collect and analyze electronic messagesto model predictive responses, according to some examples. Diagram 400includes a response platform 460 configured to generate a data modelbased on analyses of historic electronic messages and correspondingdispositions thereof (e.g., whether a message type is predicted to causeor invoke a responsive action, including generating and transmitting aresponse electronic message 401 c). Response platform 460 includes adata collector 430 and a message generator 462. Further, data collector430 is shown to include an analyzer 434, which, in turn, includes acomponent characterizer 472, a data correlator 474, and a messagecharacterizer 437, any of which may be implemented in hardware orsoftware, or a combination of both. In one or more implementations,elements depicted in diagram 400 of FIG. 4 may include structures and/orfunctions as similarly-named or similarly-numbered elements depicted inother drawings. Further, structures and/or functions of elementsdepicted in diagram 400 of FIG. 4 are presented as merely oneinstructive example to form, use, and update a data model stored inrepository 444. Thus, diagram 400 depicts but one of various otherimplementations to form, use, and update a data model for predictinggeneration of a response as a function of characterized messagecomponents, among other things.

Analyzer 434 may be configured to data mine and analyze relatively largenumber of datasets with hundreds, thousands, millions, or anyinnumerable amount of data points having multiple dimensions, variables,or attributes. Further, analyzer 434 may be configured to correlate oneor more attributes or subsets of data, as datasets 423, to one or moreclassification values 429 a to 429 c so that generation of a responseelectronic message may be predicted based on a particular electronicmessage and a classification value.

Component characterizer 472 may be configured to receive data 401 arepresenting electronic messages and any other source of data from whichcomponents (e.g., words, phrases, topics, etc.) of one or more subsetsof electronic messages (e.g., published messages) may be extracted andcharacterized. In some examples, component characterizer 472 may beconfigured to identify attributes that may be characterized to determinevalues, qualities, or characteristics of an attribute. For instance,component characterizer 472 may determine attributes or characteristicthat may include a word, a phrase, a topic, or any message attribute,and can describe the component and a corresponding value as metadata.Message attributes (or component characteristics) may be expressed inany value or data type, such as numeric, string, logical, etc. A messageattribute may include metadata that describes, for example, a languageassociated with the word (e.g., a word is in Spanish), or any otherdescriptor, such as a number of messages received from a particularsender over an interval of time (e.g., indicative of urgency), a topic(e.g., “alcohol,” “store hours,” “product name X,” “modify order,” etc.)and the like. In some examples, component characterizer 472 mayimplement at least structural and/or functional portions of a messagecomponent attribute determinator 233 of FIG. 2.

Data correlator 474 may be configured to statistically analyzecomponents and attributes of electronic messages to identify predictiverelationships between, for example, an attribute and a value predictinga likelihood that a received electronic message may invoke a responsemessage. According to some embodiments, data correlator 474 may beconfigured to classify and/or quantify various “attributes” and/or“received electronic messages” by, for example, applying machinelearning or deep learning techniques, or the like. In one example, datacorrelator 474 may be configured to segregate, separate, or distinguisha number of data points (e.g., vector data) representing similar (orstatistically similar) attributes or received electronic messages,thereby forming one or more sets of clustered data 422, each of whichmay include one or more clusters 423 of data (e.g., in 3-4 groupings ofdata). Clusters 423 of data may be grouped or clustered about aparticular attribute of the data, such as a source of data (e.g., achannel of data), a type of language, a degree of similarity withsynonyms or other words, etc., or any other attribute, characteristic,parameter or the like. In at least one example, each cluster 423 of datamay define a subset of electronic messages having one or moresimilarities (e.g., a statistically same topic). For example, electronicmessages associated with cluster 423 may relate to “payment related”message content, and may have one or more similar attributes havingsimilar attribute values.

According to some examples, data correlator 474 may identify one ofdatasets 424 for at least one cluster 423, whereby dataset 424 a mayinclude a subset of attribute values. For example, dataset 424 a may beassociated with predominant values of “TW” (e.g., Twitter™) for achannel type attribute, “store hours” for a topic attribute, “5” as “anumber of messages sent per a user” attribute, and “EN” as a languageattribute. Note that diagram 500 depicts use of these attributes andvalues in FIG. 5. Referring back to FIG. 4, data correlator 474 may formdataset 424 as a correlated dataset 424 a by computing and associating aclassification value 429 c to one of datasets 424. Data correlator 474,therefore, may be configured to analyze past actions, activities, andbehaviors that are recorded or stored in message data repository 442 fordataset 424 a (and/or cluster 423) to compute a likelihood that a nextreceived electronic message may be similar to dataset 424 a. may havecommon or similar likelihoods. If similar, both In this example, datacorrelator 474 may determine that over 10,000 previously-receivedelectronic messages are associated with dataset 424 a, and that 9,100response messages have been generated responsive to the 10,000previously-received electronic messages. Thus, at least in this example,a classification value (“91%”) 429 c (e.g., 10,000/9,100) may be linkedto dataset 424 a, thereby forming a correlated dataset 424 a. Hence, acorrelated dataset may be described as being linked to a valueindicative of a predicted action. According to various examples,clustered data 422, correlated datasets 424, and classification values429 a to 429 c, as well as threshold values, one or more of which mayconstitute a data model that is be stored in model data repository 444.

While any number of techniques may be implemented, data correlator 474may apply “k-means clustering,” or any other clustering dataidentification techniques to form clustered data 422 and clusteranalysis thereof. In some examples, data correlator 474 maybe configuredto detect patterns or classifications among datasets 424 and other datathrough the use of Bayesian networks, clustering analysis, as well asother known machine learning techniques or deep-learning techniques(e.g., including any known artificial intelligence techniques, or any ofk-NN algorithms, linear support vector machine (“SVM”) algorithm,regression and variants thereof (e.g., linear regression, non-linearregression, etc.), Bayesian inferences and the like, includingclassification algorithms, such as Naïve Bayes classifiers, or any otherstatistical or empirical technique).

Message characterizer 437 is shown to include a classifier 438, which inturn, may include a pattern matcher 499, and a response predictor 439.In some examples, message characterizer 437 may implement at leaststructural and/or functional portions of a message characterizer 237 ofFIG. 2. Message characterizer 437 may be configured to characterize a“newly-received” message for comparison against a data model to form aset of characterized data 425. Thus, classifier 438 may be configured toidentify attributes and corresponding attributes that may be matched, asa data pattern, against patterns of data including correlated datasets424 and the like. Consider that pattern matcher 499 determines thatcharacterized data 425 matches correlated dataset 424 a, which may beassociated with a classification value of 91%. Thus, characterized data425 for a “newly-received” message may be linked to, or otherwisedescribed as having, a likelihood of 91% with which a response messageis predicted. Response predictor 439 may be configured, at least in somecases, to compare classification value 429 c to a threshold value withwhich to test whether an action, such as generating a response message,is predicted to occur. For example, if a threshold value for generatinga response is 85% and classification value 429 c of 91%, is greater(which it is in this example), then response predictor 439 predicts (andcauses) generation of a response message.

Message generator 462 may be configured to generate a response message401 c automatically or based on user input. A response electronicmessage may be formatted for transmission as data 401 c via networks 411to any number of social media network computing devices. Responsemessage 401 c and a user input signal generated at user input 419 arefed back, prior to sending, into electronic message response platform460. In this case, while a received message has been predicted togenerate a response with a 91% likelihood, user 421 selected “No” to aquestion whether to respond in user interface 418 of computing device409. As the user-initiated action is contrary to a predicted action,analyzer 434 may be configured to adapt the data model based on thenewly-received electronic message and its attributes and datasets 424.As such, a data model stored in repository 444 may be recalibrated toadapt to a recorded action of “dismiss” (by selecting user input 419).In various examples, analyzer 434 may be configured to continuouslyadapt a data model as, for example, different data patterns arerecognized as a result of different content in received electronicmessages.

FIG. 5 is a diagram depicting an example of a data correlator and amessage characterizer configured to determine a predictive response,according to some embodiments. Note that diagram 500 is illustrative ofone example by which to determine whether generation of a responseelectronic message may be predicted, and, as such, diagram 500 is notintended to limit the prediction of responses to structural elementsand/or functional features described in FIG. 5. According to the exampleshown, diagram 500 includes a data correlator 754 configured to identifydatasets, and a message characterizer 537 that may be configured tocharacterize an incoming electronic message and predict whether aresponse may be generated. Message characterizer 537 is shown to includea pattern matcher 599, a response predictor 539, and a similaritydetector 597. In one or more implementations, elements depicted indiagram 500 of FIG. 5 may include structures and/or functions assimilarly-named or similarly-numbered elements depicted in otherdrawings.

Data correlator 574 may be configured to form or otherwise identifydatasets with which to use in comparisons. To convey a quality of adataset and associated attribute values, “a shape” for each of threedatasets are shown mapped to a parallel coordinate plot 502. As shown,cluster (“1”) 523 a of clustered data 522 is mapped to a curve or plot504, cluster (“2”) 523 b is mapped to plot 506, and cluster (“3”) 523 cis mapped to plot 508. In some cases, plots 502, 504, and 506 mayrepresent median attribute values (regardless of whether the values arenumeric, string, etc.). Therefore, plot 502 illustrates visually aresult of data correlator 574 being configured to form and identifydatasets based on characterized attribute data, whereby plot 502 mayconvey the degrees of similarities and differences among severaldatasets (and clusters).

Message characterizer 537 may be configured to characterize a“newly-received” message for comparison against a data model, whichincludes data set forth in plot 502, to form a set of characterizeddata. Also, message characterizer 537 may be configured to identifyattributes that may be matched, as a data pattern (e.g., in plot 503),against patterns of data in plots 504, 506, and 508. In this example, anewly-received message may be characterized as having attributes andattribute values set forth a data pattern 510. According to someexamples, pattern matcher 599 may be configured to match data pattern510 against plots 504, 506, and 508 to determine which of plots 504 to508 are similar to plot 510. For example, pattern matcher 599 mayimplement curve matching or fitting techniques, or other regressiontechniques to determine whether plot 510 best matches either plot 504 or506.

Response predictor 539 may be configured to initiate a “response” action524 or take no action (e.g., “no response”) 526. Further to the exampleshown, consider that data pattern 506 is linked to or otherwiseassociated with a classification value indicative of predicting aresponse, whereas data pattern 504 may be linked to or otherwiseassociated with another classification value indicative of dismissing amessage relative to a threshold value. So, if pattern matcher 599determines that data pattern 510 may be most similar to data pattern506, then a response 524 may be predicted.

Similarity detector 597 may be configured to determine a degree ofsimilarity of a pattern of data to the dataset. In one implementation, avalue representing a likelihood of a response (e.g., classificationvalue) may be modified as a function of the degree of similarity.Alternatively, a threshold value may be modified based on a computeddegree of similarity. So, for example, if an electronic message isassociated with a likelihood of response of “0.80,” but has a degree ofsimilarity of “0.70” (e.g., 70% similar to a dataset), then thethreshold value may change to a lesser value to increase a tolerance(e.g., to identify false negatives). Thus, at least in some cases, ahigher degree of similarity may correlate to increased precision inpredicting a response message is to be generated. Again, theabove-described functionalities of data correlator 574 and messagecharacterizer 537, in relation to depicted plots 502 and 503, areintended to be instructive of one of many ways of performing the variousimplementations described herein.

FIG. 6 is a flow diagram as an example of forming a set of patterneddata to predict whether an electronic message generates a response,according to some embodiments. Flow 600 may be an example of forming amodel based on historic behavior and/or activity (e.g., based on whichpattern of data is associated with or invokes a response). At 602, datarepresenting a sample of electronic messages are received into an entitycomputing system to form, for example, a data model. At 604, one or morecomponents of an electronic message and respective componentcharacteristic values may be identified or determined as attributes. At606, an electronic message may be characterized by, for example,characterizing one or more component characteristics to identify whethera characterized message is associated with a dataset formed by a datamodel (e.g., during a first time interval in which a model is formed).Further to 606, a data model may be formed so that patterns of data maybe identified. The patterns of data can be compare against one or morecomponent characteristics to, for example, determine a match between apattern of data to a subset of component characteristics to determinethe dataset. The subset of component characteristics may be associatedwith likelihood of generating a response. According to some examples, adegree of similarity of a pattern of data to the dataset may bedetermined. For example, curve matching or fitting techniques, as wellas regression techniques and the like may be used to quantify similarityof one electronic message against a dataset. In one implementation, avalue representing a likelihood of a response may be modified as afunction of the degree of similarity. For example, a threshold value maybe modified based on a computed degree of similarity. So if anelectronic message is associated with a likelihood of response of“0.80,” but has a degree of similarity of “0.70” (e.g., 70% similar to adataset), then the threshold value may change to a lesser value toincrease a tolerance (e.g., to identify false negatives).

At 608, a frequency with which response electronic messages based on anelectronic message being associated with the dataset may be analyzed.For example, a subset of electronic messages in which each messagematches (or substantially matches) a pattern of data (e.g., a dataset)may be associated with a rate of response, such as 80%. Thus, any futureelectronic message that matches that pattern of data may be predicted togenerate a response, for example, 80% of the time. Hence, the rate ofresponse may be used to predict a value representing a likelihood of aresponse being generated based on the frequency. According to someexamples, transmission of a response electronic message may be detectedto feed information back into the data model. Thus, a value representinga frequency may be modified responsive to include the response, wherebya data model may be recalibrated to modify a likelihood of a responsefor a subsequent electronic message (e.g., during a second interval oftime in which a model is being used with feedback). At 610, anelectronic message that generates a response may be classified to form aclassified message. At 612, a computing device may be invoked totransmit a response electronic message based on the classification ofthe classified message. Note that in some examples, a computing deviceneed not be invoked to transmit a response electronic message. Thus, aresponse electronic message not be transmitted, for example, if thevalue of transmitting the message is negligible or of no value. In thiscase, a response electronic message need not be transmitted.

FIG. 7 is a diagram depicting an example of a user interface configuredto accept data signals to visually identify and/or interact with asubset of messages predicted to generate a response, according to someexamples. Diagram 700 includes a user interface 702 configured to depictor present data representing a message queue 760 with which to formulatea response or refine the process of forming a response electronicmessage, according to some examples.

As shown, interface 702 depicts a graphical representation 720 ofmessage queue 760, which presents summaries 770, 772, and 774 of eachreceived message having a message identifier (“ID”) 761, a topic 765 asan attribute value, and a predictive value 767 (i.e., a classificationvalue). Interface 702 also includes a threshold value (“80”) 725, whichis selectably adjustable up or down via selecting device 726. Modifyingthreshold value 725 forms another threshold value that, for example, mayenhance tolerance during matching of electronic messages against a datamodel. Also, modifying threshold value 725 may modify an amount of falsenegatives that may be detected and corrected (e.g., by way of audit). Insome cases, selecting device 726 may activate input 790, which may beconfigured to perform refined processing, such as routing one ofmessages 770, 772, and 774 to another computing device based on anattribute, such as language, topic, etc. Selecting device 726 may alsobe used to activate input 791, which may be used to reevaluatepredictive value 767 of a message, such as messages 776 and 778. In somecases, these messages may not be predicted to generate a response, and,thus, dismissed. However, activation of input 791 may override apredicted dismissal of such messages. Note that one or more of thefunctionalities described in diagram 700 may be performed automatically.

FIG. 8 is a diagram depicting an example of a user interface configuredto aggregation of messages predicted to at least generate a response,according to some examples. Diagram 800 includes a user interface 802configured to depict or present data representing a first graphicalrepresentation 820 to depict a number of response messages transmitted(e.g., based on likelihood of received messages predicted to invoke suchresponses), according to some examples. Note, too, that a secondgraphical representation 830 depicts a number of messages dismissed. Insome cases, the dismissed messages need not be analyzed by computingdevice or reviewed by an agent-user, thereby conserving resources andenhancing responsiveness, among other things.

FIG. 9 illustrates examples of various computing platforms configured toprovide various functionalities to components of an electronic messageresponse management platform 900, which may be used to implementcomputer programs, applications, methods, processes, algorithms, orother software, as well as any hardware implementation thereof, toperform the above-described techniques.

In some cases, computing platform 900 or any portion (e.g., anystructural or functional portion) can be disposed in any device, such asa computing device 990 a, mobile computing device 990 b, and/or aprocessing circuit in association with initiating any of thefunctionalities described herein, via user interfaces and user interfaceelements, according to various examples.

Computing platform 900 includes a bus 902 or other communicationmechanism for communicating information, which interconnects subsystemsand devices, such as processor 904, system memory 906 (e.g., RAM, etc.),storage device 908 (e.g., ROM, etc.), an in-memory cache (which may beimplemented in RAM 906 or other portions of computing platform 900), acommunication interface 913 (e.g., an Ethernet or wireless controller, aBluetooth controller, NFC logic, etc.) to facilitate communications viaa port on communication link 921 to communicate, for example, with acomputing device, including mobile computing and/or communicationdevices with processors, including database devices (e.g., storagedevices configured to store atomized datasets, including, but notlimited to triplestores, etc.). Processor 904 can be implemented as oneor more graphics processing units (“GPUs”), as one or more centralprocessing units (“CPUs”), such as those manufactured by Intel®Corporation, or as one or more virtual processors, as well as anycombination of CPUs and virtual processors. Computing platform 900exchanges data representing inputs and outputs via input-and-outputdevices 901, including, but not limited to, keyboards, mice, audioinputs (e.g., speech-to-text driven devices), user interfaces, displays,monitors, cursors, touch-sensitive displays, LCD or LED displays, andother I/O-related devices.

Note that in some examples, input-and-output devices 901 may beimplemented as, or otherwise substituted with, a user interface in acomputing device associated with, for example, a user account identifierin accordance with the various examples described herein.

According to some examples, computing platform 900 performs specificoperations by processor 904 executing one or more sequences of one ormore instructions stored in system memory 906, and computing platform900 can be implemented in a client-server arrangement, peer-to-peerarrangement, or as any mobile computing device, including smart phonesand the like. Such instructions or data may be read into system memory906 from another computer readable medium, such as storage device 908.In some examples, hard-wired circuitry may be used in place of or incombination with software instructions for implementation. Instructionsmay be embedded in software or firmware. The term “computer readablemedium” refers to any tangible medium that participates in providinginstructions to processor 904 for execution. Such a medium may take manyforms, including but not limited to, non-volatile media and volatilemedia. Non-volatile media includes, for example, optical or magneticdisks and the like. Volatile media includes dynamic memory, such assystem memory 906.

Known forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer can access data. Instructions may further betransmitted or received using a transmission medium. The term“transmission medium” may include any tangible or intangible medium thatis capable of storing, encoding or carrying instructions for executionby the machine, and includes digital or analog communications signals orother intangible medium to facilitate communication of suchinstructions. Transmission media includes coaxial cables, copper wire,and fiber optics, including wires that comprise bus 902 for transmittinga computer data signal.

In some examples, execution of the sequences of instructions may beperformed by computing platform 900. According to some examples,computing platform 900 can be coupled by communication link 921 (e.g., awired network, such as LAN, PSTN, or any wireless network, includingWiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.)to any other processor to perform the sequence of instructions incoordination with (or asynchronous to) one another. Computing platform900 may transmit and receive messages, data, and instructions, includingprogram code (e.g., application code) through communication link 921 andcommunication interface 913. Received program code may be executed byprocessor 904 as it is received, and/or stored in memory 906 or othernon-volatile storage for later execution.

In the example shown, system memory 906 can include various modules thatinclude executable instructions to implement functionalities describedherein. System memory 906 may include an operating system (“O/S”) 932,as well as an application 936 and/or logic module(s) 959. In the exampleshown in FIG. 9, system memory 906 may include any number of modules959, any of which, or one or more portions of which, can be configuredto facilitate any one or more components of a computing system (e.g., aclient computing system, a server computing system, etc.) byimplementing one or more functions described herein.

The structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or acombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated with one ormore other structures or elements. Alternatively, the elements and theirfunctionality may be subdivided into constituent sub-elements, if any.As software, the above-described techniques may be implemented usingvarious types of programming or formatting languages, frameworks,syntax, applications, protocols, objects, or techniques. As hardwareand/or firmware, the above-described techniques may be implemented usingvarious types of programming or integrated circuit design languages,including hardware description languages, such as any register transferlanguage (“RTL”) configured to design field-programmable gate arrays(“FPGAs”), application-specific integrated circuits (“ASICs”), or anyother type of integrated circuit. According to some embodiments, theterm “module” can refer, for example, to an algorithm or a portionthereof, and/or logic implemented in either hardware circuitry orsoftware, or a combination thereof. These can be varied and are notlimited to the examples or descriptions provided.

In some embodiments, modules 959 of FIG. 9, or one or more of theircomponents, or any process or device described herein, can be incommunication (e.g., wired or wirelessly) with a mobile device, such asa mobile phone or computing device, or can be disposed therein.

In some cases, a mobile device, or any networked computing device (notshown) in communication with one or more modules 959 or one or more ofits/their components (or any process or device described herein), canprovide at least some of the structures and/or functions of any of thefeatures described herein. As depicted in the above-described figures,the structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or anycombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated or combinedwith one or more other structures or elements. Alternatively, theelements and their functionality may be subdivided into constituentsub-elements, if any. As software, at least some of the above-describedtechniques may be implemented using various types of programming orformatting languages, frameworks, syntax, applications, protocols,objects, or techniques. For example, at least one of the elementsdepicted in any of the figures can represent one or more algorithms. Or,at least one of the elements can represent a portion of logic includinga portion of hardware configured to provide constituent structuresand/or functionalities.

For example, modules 959 or one or more of its/their components, or anyprocess or device described herein, can be implemented in one or morecomputing devices (i.e., any mobile computing device, such as a wearabledevice, such as a hat or headband, or mobile phone, whether worn orcarried) that include one or more processors configured to execute oneor more algorithms in memory. Thus, at least some of the elements in theabove-described figures can represent one or more algorithms. Or, atleast one of the elements can represent a portion of logic including aportion of hardware configured to provide constituent structures and/orfunctionalities. These can be varied and are not limited to the examplesor descriptions provided.

As hardware and/or firmware, the above-described structures andtechniques can be implemented using various types of programming orintegrated circuit design languages, including hardware descriptionlanguages, such as any register transfer language (“RTL”) configured todesign field-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs”), multi-chip modules, or any other type ofintegrated circuit. For example, modules 959 or one or more of its/theircomponents, or any process or device described herein, can beimplemented in one or more computing devices that include one or morecircuits. Thus, at least one of the elements in the above-describedfigures can represent one or more components of hardware. Or, at leastone of the elements can represent a portion of logic including a portionof a circuit configured to provide constituent structures and/orfunctionalities.

According to some embodiments, the term “circuit” can refer, forexample, to any system including a number of components through whichcurrent flows to perform one or more functions, the components includingdiscrete and complex components. Examples of discrete components includetransistors, resistors, capacitors, inductors, diodes, and the like, andexamples of complex components include memory, processors, analogcircuits, digital circuits, and the like, including field-programmablegate arrays (“FPGAs”), application-specific integrated circuits(“ASICs”). Therefore, a circuit can include a system of electroniccomponents and logic components (e.g., logic configured to executeinstructions, such that a group of executable instructions of analgorithm, for example, and, thus, is a component of a circuit).According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof (i.e., a modulecan be implemented as a circuit). In some embodiments, algorithms and/orthe memory in which the algorithms are stored are “components” of acircuit. Thus, the term “circuit” can also refer, for example, to asystem of components, including algorithms. These can be varied and arenot limited to the examples or descriptions provided.

Although the foregoing examples have been described in some detail forpurposes of clarity of understanding, the above-described inventivetechniques are not limited to the details provided. There are manyalternative ways of implementing the above-described inventiontechniques. The disclosed examples are illustrative and not restrictive.

1. A system, comprising: a memory configured to store executableinstructions; and a processor configured to receive data representing anelectronic message including other data representing an item associatedwith an entity computing system associated with an electronic messagingaccount, to identify one or more component characteristics associatedwith one or more components of the electronic message, wherein the oneor more component characteristics are each representative of a componentcharacteristic value comprising data representing one or more of alanguage, a word, and a topic specifying a product or a service, aquality issue, or a payment issue, to characterize the electronicmessage based on the one or more component characteristics to classifythe electronic message for a response as a classified message, whereinthe processor is further configured to characterize the electronicmessage to determine at least one classification value that specifiesgeneration of a responsive electronic message, to retrieve a firstthreshold value from data storage against which to compare with a firstvalue to classify the electronic message, wherein the first valuerepresents a probability derived from the one or more componentcharacteristics, to compare the first threshold value to the firstvalue, and to classify the first value as a first classification value,wherein the first classification value represents a likelihood of anaction, to match a subset of patterns of data from the electronicmessage against a data model being associated with a likelihood that aspecific pattern of data causes a response implemented in the responsiveelectronic message wherein the data model includes at least patterns ofdata corresponding to the one or more component characteristics, tocause a computing device including a user interface to perform an actionto facilitate the response to the classified message, and to present auser input on the user interface configured to accept a data signal toinitiate the action.
 2. The system of claim 1, wherein the processor isconfigured to: identify a second threshold value against which tocompare with a first value 20 to classify the electronic message; modifythe second threshold value to vary from a first threshold value; andclassify the first value as a second classification value.
 3. The systemof claim 1, wherein the processor is configured to: identify a secondthreshold value against which to compare with a first value 20 toclassify the electronic message; modify the second threshold value tovary from a first threshold value; classify the first value as a secondclassification value; cause evaluation of the electronic message withthe second classification value; identify the second classificationvalue as a false negative; and reclassify the electronic message toassociate with the first classification value to form a reclassifiedelectronic message.
 4. The system of claim 1, wherein the processor isconfigured to: identify a second threshold value against which tocompare with a first value 20 to classify the electronic message; modifythe second threshold value to vary from a first threshold value;classify the first value as a second classification value; causeevaluation of the electronic message with the second classificationvalue; identify the second classification value as a false negative;reclassify the electronic message to associate with the firstclassification value to form a reclassified electronic message; andrecalibrate a data model configured to characterize the electronicmessage based on the reclassified electronic message to increaseprediction of the first classification value.
 5. The system of claim 1,wherein the processor is further configured to: identify a secondthreshold value against which to compare with a first value 20 toclassify the electronic message; modify the second threshold value tovary from a first threshold value; classify the first value as a secondclassification value; cause evaluation of the electronic message withthe second classification value; identify the second classificationvalue as a false negative; reclassify the electronic message toassociate with the first classification value to form a reclassifiedelectronic message; recalibrate a data model configured to characterizethe electronic message based on the reclassified electronic message toincrease prediction of the first classification value; identify a thirdthreshold value against which to compare with a first value to classifythe electronic message; compare the third threshold value to the firstvalue; and classify the first value as a third classification value. 6.The system of claim 1, wherein the processor is further configured to:identify a second threshold value against which to compare with a firstvalue 20 to classify the electronic message; modify the second thresholdvalue to vary from a first threshold value; classify the first value asa second classification value; cause evaluation of the electronicmessage with the second classification value; identify the secondclassification value as a false negative; reclassify the electronicmessage to associate with the first classification value to form areclassified electronic message; recalibrate a data model configured tocharacterize the electronic message based on the reclassified electronicmessage to increase prediction of the first classification value;identify a third threshold value against which to compare with a firstvalue to classify the electronic message; compare the third thresholdvalue to the first value; classify the first value as a thirdclassification value; detect the third classification value; and dismissthe electronic message.
 7. The system of claim 1, wherein the processoris further configured to: identify a second threshold value againstwhich to compare with a first value 20 to classify the electronicmessage; modify the second threshold value to vary from a firstthreshold value; classify the first value as a second classificationvalue; cause evaluation of the electronic message with the secondclassification value; identify the second classification value as afalse negative; and reclassify the electronic message to associate withthe first classification value to form a reclassified electronicmessage; identify the one or more component characteristics, comprisingidentifying a component characteristic having a component characteristicvalue; and perform the action as a function of the componentcharacteristic value.
 8. The system of claim 7, wherein the processor isfurther configured to route the electronic message to an agent computingsystem to generate a response.
 9. The system of claim 1, wherein theprocessor is further configured to: classify the electronic message ashaving a first classification in which a response to the electronicmessage is generated; and generating a responsive electronic message.10. The system of claim 1, wherein the action is a generated electronicmessage responsive to at least one of the components of the electronicmessage.
 11. The system of claim 1, wherein the electronic messageincludes data representing transmission to the electronic messagingaccount.
 12. The system of claim 1, wherein the electronic messageincludes data representing transmission to a third party computingsystem having a third party electronic messaging account.
 13. A system,comprising: a memory configured to store data; and a processorconfigured to receive the data representing electronic messages into anentity computing system associated with an electronic messaging account,to determine one or more components of the electronic message andrespective component characteristic values as attributes wherein the oneor more component characteristics are each represented by a componentcharacteristic value comprising data representing one or more of alanguage, a word, and a topic specifying a product or a service, aquality issue, or a payment issue, to characterize the electronicmessage based on the one or more component characteristics during afirst time interval as associated with a dataset formed by a data model,wherein to characterize the electronic message determines at least oneclassification value that specifies generation of a responsiveelectronic message, to match a subset of patterns of data from theelectronic message against the dataset being associated with alikelihood that a specific pattern of data causes a response implementedin the responsive electronic message, to analyze a frequency with whichresponse electronic messages based on an electronic message beingassociated with the dataset, to predict a value representing alikelihood of a response being generated based on the frequency, and theone or more component characteristics, wherein to predict the valuefurther comprises retrieving a first threshold value from data storageagainst which to compare with a first value to classify the electronicmessage, wherein the first value represents a probability derived fromthe one or more component characteristics, to compare the firstthreshold value to the first value, to classify the first value as thepredicted value, to classify the electronic message for a response as aclassified message, and to cause a computing device to transmit aresponse electronic message.
 14. The system of claim 13, wherein theprocessor is further configured to characterize the electronic messageto form the data model, to identify a pattern of data against which tocompare the one or more component characteristics, and to match apattern of data to a subset of component characteristics to determinethe dataset.
 15. The system of claim 14, wherein the processor isfurther configured to determine a degree of similarity of the pattern ofdata to the dataset.
 16. The system of claim 14, wherein the processoris further configured to adjust the value representing a likelihood of aresponse as a function of the degree of similarity.
 17. The system ofclaim 13, wherein the processor is further configured to detecting theresponse electronic message is transmitted.
 18. The system of claim 13,wherein the processor is further configured to modify a valuerepresenting the frequency responsive to include the response.
 19. Thesystem of claim 13, wherein the processor is further configured toupdate the data model to recalibrate the likelihood of a response for asubsequent electronic message during a second interval of time.